{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import argparse\n",
    "import sys\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "FLAGS = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-2-a4e982e2d15a>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda2\\envs\\python3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda2\\envs\\python3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda2\\envs\\python3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda2\\envs\\python3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda2\\envs\\python3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "data_dir = '/tmp/tensorflow/mnist/input_data'\n",
    "mnist = input_data.read_data_sets(data_dir, one_hot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h1_units = 400\n",
    "#h2_units = 500\n",
    "\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "\n",
    "w1 = tf.Variable(tf.truncated_normal([784, h1_units], stddev = 0.1))\n",
    "#w1 = tf.Variable(tf.zeros([784, h1_units]))\n",
    "b1 = tf.Variable(tf.zeros([h1_units]))\n",
    "\n",
    "#w2 = tf.Variable(tf.truncated_normal([h1_units, h2_units], stddev = 0.1))\n",
    "#b2 = tf.Variable(tf.zeros([h2_units]))\n",
    "\n",
    "w2 = tf.Variable(tf.zeros([h1_units, 10]))\n",
    "b2 = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "h1 = tf.nn.relu(tf.matmul(x, w1) + b1)\n",
    "#h1 = tf.nn.sigmoid(tf.matmul(x, w1) + b1)\n",
    "#h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)\n",
    "y = tf.nn.softmax(tf.matmul(h1, w2) + b2)\n",
    "\n",
    "y_ = tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))\n",
    "\n",
    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y) , reduction_indices=[1]))\n",
    "train_step = tf.train.GradientDescentOptimizer(0.8).minimize(cross_entropy)\n",
    "\n",
    "sess = tf.Session()\n",
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(3000):\n",
    "    batch_xs, batch_ys = mnist.train.next_batch(100)\n",
    "    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9799\n"
     ]
    }
   ],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "#print(sess.run(accuracy, feed_dict={x: mnist.train.images, y_: mnist.train.labels}))\n",
    "print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 优化思路\n",
    "## 1、学习率，从0.5 调到 0.8 ，准确率从 0.9546提升到 0.9694\n",
    "## 2、激活函数，从sigmoid 换成 relu,准确率从 0.9694 提升到 0.9807\n",
    "## 3、尝试增加2层隐层，但效果不好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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